How engineers use AI, how to do it well, and what it means for your job. Written for people who ship and run the software, not people who build models.
Meta's Muse quietly used an OpenAI model on Azure. Here's what that reveals about the hidden markups stacked into every AI-native SaaS subscription.
OrcaSAQ2 27B and Bonsai 2 27B run agent loops on a laptop for near-zero cost. Here's what their own benchmarks say about the agentic accuracy tradeoff.
AI coding agents can cost $13 a day or $31,000 a month. Here's the token math behind runaway bills and the controls that keep spend predictable.
From Klarna's support pivot to Zoo.dev's CAD agent, real cases of AI agents replacing SaaS, and why full replacement still has limits.
RLCD trains models like Jev to output calibrated decisions, not text. Here's how it works, the real speed and cost numbers, and why 'can't hallucinate' isn't 'can't be wrong.'
Cline Desktop and Whiteboard show what an open-source, agent-native IDE requires: model flexibility, self-hosting, and real governance controls.
AI coding tools hallucinate open-source packages, and attackers register them with malware. Here's how to secure AI-generated code before slopsquatting reaches production.
Cursor, Claude Code, and Copilot all rationed power users in 2025-2026. Here's why self-hosted open-weight coding models are the real alternative.
The research is consistent: AI code ships faster and breaks more. Here's the churn, duplication, and complexity toolkit that quantifies it.
Employee-built AI automations can spike your API bill fast. Here are the trigger, budget, and caching fixes that cut AI API usage costs 70-90%.
Claude Opus 5.5 wins on raw coding capability. GPT-6 Sol wins on cost per task. Here's what that means for building internal tools instead of buying SaaS.
Pizza Bot and Grok Bot made employee-built AI agents trivial to deploy. Here's how to inventory, own, and govern them instead of trying to ban them.
AI hit IMO gold medal reasoning in 2025. That same machinery now verifies code correctness, changing the risk math behind build vs. buy decisions.
Jev-class decision models trade text generation for typed choices, scores, and probabilities, running 40-400x cheaper and up to 200x faster than chat LLMs.
Omarchy pre-wires ten AI agent CLIs into Arch Linux at the OS level. What it actually is, what it isn't, and who controls the config and the money.
Agentic workflows burn 5 to 1,000x more tokens than chat. Here's the caching, code execution, routing, and self-hosting playbook that cuts agent bills 40 to 90 percent.
Old laptops can run local AI. See how Omarchy Linux and Ollama turn a decade-old, GPU-less machine into a working agentic coding setup.
Priority tiers and surge multipliers are repricing AI and API access in real time. Here's what congestion pricing means for SaaS budgets and the build-vs-buy decision.
What is a System 1 AI model versus JEV? A grounded look at fast, calibrated classifiers versus reasoning LLMs for GUI and form-filling agents.
Open-weight models like gpt-oss-120b now run on enterprise hardware from $2K to $30K. Here's the playbook and the real break-even math against your SaaS bill.
Banks, hospitals, and governments are downloading open-weight models and running them on owned infrastructure. Here's the real math on local AI vs API.
SUSE, Windows, and Ubuntu are baking agentic AI and MCP into the OS itself. Here's what an agentic operating system means for build vs. buy in 2026.
AI agents now outpace CI/CD, review, and staging. The data on the bottleneck, what an intent conflict is, and the five fixes teams are building.
MCP connects AI to tools. Orchestrators coordinate agents. Here's the real comparison, the security data, and why production systems need both plus governance.